In recent years, agent-based computational models have been used to study financial markets. One of the most interesting elements involved in these studies is the process of learning, in which market participants try to obtain information from the market in order to improve their strategies and hence increase their profits. While in other papers it has been shown how this learning process is determined by factors such as the adaptation period, the composition of the market and the intensity of the signals that an agent can perceive, in this paper we shall discuss the effect of external information in the learning process in an artificial financial market (AFM). In particular, we will analyze the case when external information is such that it forces all participants to randomly revise their expectations of the future. Even though AMFs usually use sophisticated artificial intelligence techniques, in this study we show how interesting results can be obtained using a quite elementary genetic algorithm.